MLMatch: Multi-layer Image Matching

Image matching is one of the hottest research topics in the field of computer vision, and it aims to compute the correct feature correspondences between images. However, under the influence of illumination changes and scale changes, relying only on the similarity between feature descriptors as the basis for judging feature matching tends to generate more false feature correspondences. To solve the above problems, in this paper, we systematically analyze various factors that lead to false feature correspondences, design corresponding solutions for these various factors, and then propose a multi-layer image matching (MLMatch) method. Firstly, eliminating the potential mismatches caused by brute force matching; secondly, eliminating the mismatches caused by the ambiguity of descriptors; and then, eliminating the feature matches that do not satisfy geometric constraints. Finally, we test the proposed MLMatch method on several open-source datasets and make a comprehensive comparison with the classical methods. Experimental results show that the MLMatch method can not only compute more true feature correspondences but also has a low computational cost.